paragraph40.75ex plus1ex minus.2ex-1emFully unsupervised semantic segmentation
For CBFE and CD we take PVOC12 train to find good hyperparameter configurations, clustering granularities \(K\) , the precision threshold for CBFE as well as Markov time and the co-occurrence probability threshold for CD.
We use a segmentation of our embedding space to 200 clusters as we found this granularity to work best on PVOC for CBFE.
Before doing foreground-focused clustering using the cluster mask, we bilinearly interpolate the embeddings to the desired mask size.
For CBFE we report the precision thresholds used in Table REF .
To evaluate the unsupervised saliency estimator baseline method, we use the saliency head provided by the MaskContrast authors [1]}.
For the computation of the Jaccard distance we include unlabelled objects as foreground. We can identify these objects as they have a separate class in the PVOC dataset.
